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ShareScore release 0.9.0
Dataset results
15 results for “chemical environment”
Computational Supporting Information for How Chemical Environment Activates Anthralin and Molecular Oxygen for Direct Reaction
<p>The updated version of the dataset contains all original computational results, including validation of the level of theory, molecular structures, and analysis spreadsheets that are in support of our experimental observations of spontaneous reactivity of anthralin/dithranol molecule with molecular oxygen without any catalyst or co-substrate.<br> The paper was published in Journal of Organic Chemistry, 2020, 85(2), 1315–1321 (DOI: 10.1021/acs.joc.9b03133).</p> <p>In the meantime, the science was also also presented at the 8th ELSI Symposium, Tokyo Institute of Technology, Tokyo (Japan); February 3-7, 2020 in the context of molecular catalysis and their role in the chemical evolution of the building blocks of life.</p> <p>This version also has an important update that is being exclusively published here on Zenodo. The selected level of theory (MN15 functional with triple-zeta quality basis set supplemented with BOTH diffuse and polarization basis functions) is further confirmed to be one of the most reasonable one among 98 commonly used functionals.</p>
Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment
<p>Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of <em>in vitro</em> and <em>in silico</em> approaches for chemical testing and assessment.</p> <p> </p> <p> </p>
S98 | TIRECHEM | Tire-Related Chemicals in Environment from Literature
<p>This is the collection associated with list S98 TIRECHEM | Tire-Related Chemicals in Environment from Literature on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>This list includes tire-related chemicals found in the environment plus analytical data from literature studies and will be expanded as new data is published.</p> <p>v 0.2.0 : New chemicals added on 13 May 2024</p> <p> </p>
Dataset of chemicals of emerging concern detected in the marine environment in central and northern Patagonia in Chile
<p>This repository encompasses the environmental concentrations of chemicals of emerging concern (CECs), such as pesticides, pharmaceuticals, and industrial chemicals, co-occurring in surface water, porewater, and sediment throughout central and northern Patagonia, Chile.</p>
Soil chemistry dataset from the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica"
<p>Bases sum, H+Al (potential acidity), pH, phosphorous, remaining P (P-rem), sodium and total organic carbon distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The quantile and prediction interval data represent the spatial uncertainty of the predictions.</p> <p>As soon as the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica" is published, the paper will be cited here. </p> <p>The .zip file contains the following folders:</p> <p>1) soil_chemistry_antarctica: data containing the soil chemical attributes distribution</p> <p>2) soil_chemistry_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil attributes prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil attributes prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil attributes prediction</p>
Underlying data for: "Morphology as indicator of adaptive changes of model tissues in osmotically and chemically changing environments"
<p>For our publication "Morphology as indicator of adaptive changes of model tissues in osmotically and chemically changing environments" (at <a href="https://doi.org/10.1016/j.bioadv.2023.213635">DOI: 10.1016/j.bioadv.2023.213635)</a> we here provide the raw data for the included plots.</p> <p>Due to the aggregate size of the numerous microscopy images, those are provided on request.<br> Herein, we provide the data obtained by processing the microscopy images into cell nuclei positions, cell density profiles and morpological as well as topological parameters.</p>
Suspect Screening and Non-targeted Analysis of Chemical Pollutants in Botswana's Aquatic Environments
<p>Raw HRMS data associated with the paper titled </p> <p><strong><span>"Suspect Screening and Non-targeted Analysis of Chemical Pollutants in Botswana’s Aquatic Environments"</span></strong></p>
Dataset from Nature Materials paper: Amorphous nickel hydroxide shell tailors local chemical environment on platinum surface for alkaline hydrogen evolution reaction
<p>Dataset of the paper "Amorphous nickel hydroxide shell tailors local chemical environment on platinum surface for alkaline hydrogen evolution reaction" accepted in Nature Materials.</p> <p>- GCGA_inputs.zip: A zip file containing all needed input files to run a grand canonical genetic algorithm (GCGA) global optimization structure search. Note that the script would need modifications to be compatible with later version of the GOCIA package, please following the most updated instructions at https://github.com/zishengz/gocia</p> <p>- GCGA_Ni12OxHy_all_samples.db: An ASE database file containing all unique structures from the GCGA search of Ni12OxHy on a Pt(111) surface. </p> <p>- GM_Ni12O25H13.vasp: The structure of the global minimum structure from GCGA search, which is also the surface structure we focused on in this study, in VASP structure format.</p> <p>- rxn_structures.zip: A zip file containing the structures of reaction intermediates investigated in this work, in VASP structure format</p> <p> </p>
Data and Code for "Drift velocity of bacterial chemotaxis in dynamic chemical environments"
<p>Datasets and code required to recreate all results from "Drift velocity of bacterial chemotaxis in dynamic chemical environments"</p>
Figure data of "Variation in chemical composition and volatility of oxygenated organic aerosol in different rural, urban, and mountain environments"
<p>This dataset involves the data that is used for the figures in "Variation in chemical composition and volatility of oxygenated organic aerosol in different rural, urban, and mountain environments".</p>
The Effects of a Clean Room Sleeping Environment on Elemental and Chemical Concentrations in Children With Autism
ClinicalTrials.gov study NCT02195401. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data for "• Can we achieve atmospheric chemical environments in the laboratory? An integrated model-measurement approach to chamber SOA studies"
Open the record for dataset details and reuse information.
Virtual Environment With Biofeedback to Promote Awareness of Relapse Risk Among Chemically Dependent Individuals
ClinicalTrials.gov study NCT02535858. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Role of the Chemical Environment in the Pathogenesis of Inflammatory Bowel Disease
ClinicalTrials.gov study NCT03376230. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The International Space Station Has a Unique and Extreme Microbial and Chemical Environment Driven by Use Patterns
Space habitation provides unique challenges in built environments isolated from Earth. We produced a 3D map of the microbes and metabolites throughout the International Space Station (ISS), with 803 samples collected during space flight, including controls. We find that the use of each of the nine sampled modules within the ISS strongly drives the microbiology and chemistry of the habitat. Relating the microbiology to other Earth habitats, we find that, as with human microbiomes, built environment microbiomes also align naturally along an axis of industrialization, with the ISS providing an extreme example of an industrialized environment. We demonstrate the utility of culture-independent sequencing for microbial risk monitoring, especially as the location of sequencing moves to space. The resulting resource of chemistry and microbiology in the space-built environment will guide long-term efforts to maintain human health in space for longer durations.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.